Crowdsourcing and Optimal Market Design

Abstract

Mechanisms used to derive optimal allocations are typically designed assuming agents fully know their preferences. It is often impossible to duplicate optimal allocations when agents imperfectly observe object characteristics. I present a crowdsourcing mechanism to approximate optimal allocations under imperfect observations. To ensure truth-telling, agents are punished when their reports differ from the “wisdom-of-the-crowd.” Under mild conditions, this crowdsourcing-with-punishment mechanism replicates the full-information optimal allocation with probability exponentially converging to one in the size of the market, with small waste. No alternative mechanism can meaningfully do better. The proposed mechanism can be applied in many settings, including two-sided matching markets.

Keywords: Information aggregation, interdependent values, matching, costly voting

JEL Classification: D47, D82, C78, C72, D72